{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:29:46.955926233Z",
     "start_time": "2023-08-13T19:29:46.062558652Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from MyEDFImports import load_all_data, load_all_labels, get_edf_filenames, load_data_one_file, import_ecg, \\\n",
    "    load_labels_one_file, draw_hypnogram\n",
    "import MyEDFImports as m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CN223100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CP229110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CX230050.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/DG220020.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/DO223050.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/MyEDFImports.py:41: RuntimeWarning: Channel names are not unique, found duplicates for: {'CHIN EMG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(path + \"//\" + name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/LA216100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/LM230010.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/TK221110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/VC209100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/VP214110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/WD224010.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/MyEDFImports.py:41: RuntimeWarning: Channel names are not unique, found duplicates for: {'CHIN EMG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(path + \"//\" + name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<RawEDF | CN223100.edf, 1 x 15611000 (31222.0 s), ~6 kB, data not loaded> with 1561 windows\n",
      "<RawEDF | CP229110.edf, 1 x 20078000 (40156.0 s), ~6 kB, data not loaded> with 2007 windows\n",
      "<RawEDF | CX230050.edf, 1 x 17981000 (35962.0 s), ~6 kB, data not loaded> with 1798 windows\n",
      "<RawEDF | DG220020.edf, 1 x 17756000 (35512.0 s), ~6 kB, data not loaded> with 1775 windows\n",
      "<RawEDF | DO223050.edf, 1 x 18066500 (36133.0 s), ~6 kB, data not loaded> with 1806 windows\n",
      "<RawEDF | LA216100.edf, 1 x 16333500 (32667.0 s), ~6 kB, data not loaded> with 1633 windows\n",
      "<RawEDF | LM230010.edf, 1 x 17246500 (34493.0 s), ~6 kB, data not loaded> with 1724 windows\n",
      "<RawEDF | TK221110.edf, 1 x 15991000 (31982.0 s), ~6 kB, data not loaded> with 1599 windows\n",
      "<RawEDF | VC209100.edf, 1 x 18434500 (36869.0 s), ~6 kB, data not loaded> with 1843 windows\n",
      "<RawEDF | VP214110.edf, 1 x 17252500 (34505.0 s), ~6 kB, data not loaded> with 1725 windows\n",
      "<RawEDF | WD224010.edf, 1 x 17774000 (35548.0 s), ~6 kB, data not loaded> with 1777 windows\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//CN223100.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//CP229110.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//CX230050.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//DG220020.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//DO223050.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//LA216100.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//LM230010.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//TK221110.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//VC209100.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//VP214110.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//WD224010.edf_stages.txt\n"
     ]
    }
   ],
   "source": [
    "all_data = load_all_data()\n",
    "all_labels = load_all_labels()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:29:55.805031359Z",
     "start_time": "2023-08-13T19:29:46.956962628Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Hypnogram for every patient"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CN223100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CP229110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/CX230050.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/DG220020.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/DO223050.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/MyEDFImports.py:41: RuntimeWarning: Channel names are not unique, found duplicates for: {'CHIN EMG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(path + \"//\" + name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/LA216100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/LM230010.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/TK221110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/VC209100.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/VP214110.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n",
      "Extracting EDF parameters from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers/WD224010.edf...\n",
      "EDF file detected\n",
      "Setting channel info structure...\n",
      "Creating raw.info structure...\n",
      "NOTE: pick_channels() is a legacy function. New code should use inst.pick(...).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/MyEDFImports.py:41: RuntimeWarning: Channel names are not unique, found duplicates for: {'CHIN EMG'}. Applying running numbers for duplicates.\n",
      "  raw = mne.io.read_raw_edf(path + \"//\" + name)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<RawEDF | CN223100.edf, 1 x 15611000 (31222.0 s), ~6 kB, data not loaded> with 1561 windows\n",
      "<RawEDF | CP229110.edf, 1 x 20078000 (40156.0 s), ~6 kB, data not loaded> with 2007 windows\n",
      "<RawEDF | CX230050.edf, 1 x 17981000 (35962.0 s), ~6 kB, data not loaded> with 1798 windows\n",
      "<RawEDF | DG220020.edf, 1 x 17756000 (35512.0 s), ~6 kB, data not loaded> with 1775 windows\n",
      "<RawEDF | DO223050.edf, 1 x 18066500 (36133.0 s), ~6 kB, data not loaded> with 1806 windows\n",
      "<RawEDF | LA216100.edf, 1 x 16333500 (32667.0 s), ~6 kB, data not loaded> with 1633 windows\n",
      "<RawEDF | LM230010.edf, 1 x 17246500 (34493.0 s), ~6 kB, data not loaded> with 1724 windows\n",
      "<RawEDF | TK221110.edf, 1 x 15991000 (31982.0 s), ~6 kB, data not loaded> with 1599 windows\n",
      "<RawEDF | VC209100.edf, 1 x 18434500 (36869.0 s), ~6 kB, data not loaded> with 1843 windows\n",
      "<RawEDF | VP214110.edf, 1 x 17252500 (34505.0 s), ~6 kB, data not loaded> with 1725 windows\n",
      "<RawEDF | WD224010.edf, 1 x 17774000 (35548.0 s), ~6 kB, data not loaded> with 1777 windows\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//CN223100.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//CP229110.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//CX230050.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//DG220020.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//DO223050.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//LA216100.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//LM230010.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//TK221110.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//VC209100.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//VP214110.edf_stages.txt\n",
      "loading from /home/tadeusz/Desktop/Tadeusz/mgr_sleep_states/Jean-Pol_repaired_headers//WD224010.edf_stages.txt\n"
     ]
    }
   ],
   "source": [
    "edf_filenames = get_edf_filenames()\n",
    "edfs = {filename: import_ecg(filename) for filename in edf_filenames}\n",
    "data = {filename: load_data_one_file(edf) for filename, edf in edfs.items()}\n",
    "labels = {filename: load_labels_one_file(filename) for filename in edf_filenames}"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:30:01.902016779Z",
     "start_time": "2023-08-13T19:29:55.792517012Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## At the end of every file there is a part that is not considered, as it is not labeled and is not long enough to be 20s"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CN223100.edf \n",
      " edf datapoints: 15611000\n",
      " act datapoints: 15610000\n",
      "     difference 1000 (in seconds) 2\n",
      "CP229110.edf \n",
      " edf datapoints: 20078000\n",
      " act datapoints: 20070000\n",
      "     difference 8000 (in seconds) 16\n",
      "CX230050.edf \n",
      " edf datapoints: 17981000\n",
      " act datapoints: 17980000\n",
      "     difference 1000 (in seconds) 2\n",
      "DG220020.edf \n",
      " edf datapoints: 17756000\n",
      " act datapoints: 17750000\n",
      "     difference 6000 (in seconds) 12\n",
      "DO223050.edf \n",
      " edf datapoints: 18066500\n",
      " act datapoints: 18060000\n",
      "     difference 6500 (in seconds) 13\n",
      "LA216100.edf \n",
      " edf datapoints: 16333500\n",
      " act datapoints: 16330000\n",
      "     difference 3500 (in seconds) 7\n",
      "LM230010.edf \n",
      " edf datapoints: 17246500\n",
      " act datapoints: 17240000\n",
      "     difference 6500 (in seconds) 13\n",
      "TK221110.edf \n",
      " edf datapoints: 15991000\n",
      " act datapoints: 15990000\n",
      "     difference 1000 (in seconds) 2\n",
      "VC209100.edf \n",
      " edf datapoints: 18434500\n",
      " act datapoints: 18430000\n",
      "     difference 4500 (in seconds) 9\n",
      "VP214110.edf \n",
      " edf datapoints: 17252500\n",
      " act datapoints: 17250000\n",
      "     difference 2500 (in seconds) 5\n",
      "WD224010.edf \n",
      " edf datapoints: 17774000\n",
      " act datapoints: 17770000\n",
      "     difference 4000 (in seconds) 8\n"
     ]
    }
   ],
   "source": [
    "for f, l in labels.items():\n",
    "    edf_y = edfs[f][0][0]\n",
    "    edf_len = edf_y.size\n",
    "    datapoints = len(l) * 20 * 500\n",
    "    print(f'{f} \\n edf datapoints: {edf_len}')\n",
    "    print(f' act datapoints: {datapoints}')\n",
    "    print(f'     difference {edf_len - datapoints} (in seconds) {(edf_len - datapoints) // 500}')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:30:04.467065276Z",
     "start_time": "2023-08-13T19:30:01.936840827Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Example of information in the EDF"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "highpass\n",
      "0.0\n",
      "lowpass\n",
      "250.0\n",
      "meas_date\n",
      "2007-10-16 22:46:09+00:00\n",
      "chs\n",
      "[{'cal': 1.0, 'logno': 24, 'scanno': 24, 'range': 1.0, 'unit_mul': 0 (FIFF_UNITM_NONE), 'ch_name': 'ECG 1', 'unit': 107 (FIFF_UNIT_V), 'coord_frame': 4 (FIFFV_COORD_HEAD), 'coil_type': 1 (FIFFV_COIL_EEG), 'kind': 2 (FIFFV_EEG_CH), 'loc': array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])}]\n",
      "custom_ref_applied\n",
      "0 (FIFFV_MNE_CUSTOM_REF_OFF)\n",
      "sfreq\n",
      "500.0\n",
      "dev_head_t\n",
      "<Transform | MEG device->head>\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 0. 1.]]\n",
      "ch_names\n",
      "['ECG 1']\n",
      "nchan\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "for k, i in edfs['LA216100.edf'].info.items():\n",
    "    if i != [] and i is not None:\n",
    "        print(k)\n",
    "        print(i)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:30:04.473082043Z",
     "start_time": "2023-08-13T19:30:04.471170314Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hypno = data['LA216100.edf'].flatten()\n",
    "l_hypno = labels['LA216100.edf']\n",
    "l_hypno = np.repeat(l_hypno, 10000)\n",
    "assert len(l_hypno) == len(y_hypno)\n",
    "ax = m.draw_hypnogram(y_hypno, l_hypno)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:30:09.394111390Z",
     "start_time": "2023-08-13T19:30:04.474294828Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "def draw_hypnogram(y, y_stages, ax, name=None, ):\n",
    "    assert len(y) == len(y_stages)\n",
    "    ax2 = ax.twinx()\n",
    "    ax.step(y, 'b-')\n",
    "    ax2.step(y_stages, 'g-')\n",
    "    ax2.invert_yaxis()\n",
    "    ax.set_xlabel('time [s]')\n",
    "    ax.set_ylabel('Voltage [V]', color='b')\n",
    "    ax2.set_ylabel('Sleep Stage', color='g')\n",
    "    ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))\n",
    "    labels = [m.stages_names[item] if item in m.stages_names else item for item in ax2.get_yticks()]\n",
    "    ax2.set_yticklabels(labels)\n",
    "    plt.legend(['ECG', 'stage'])\n",
    "    if name is not None:\n",
    "        ax.set_title(f\"ECG of {name}\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:30:09.397501215Z",
     "start_time": "2023-08-13T19:30:09.395438136Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "for i, name in enumerate(edfs):\n",
    "    fig, ax = plt.subplots(figsize=(20, 6))\n",
    "    y = data[name].flatten()\n",
    "    y_stages = labels[name]\n",
    "    y_stages = np.repeat(y_stages, 10000)\n",
    "    draw_hypnogram(y, y_stages, ax, name)\n",
    "    plt.savefig(f'images/{name}_{len(set(y_stages))}_stages.png')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_5199/855910429.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax2.set_yticklabels(labels)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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8sund5MsQixMnmgp6u930pvBUAe+Pwtf6v/9euu0VdvHF0iuveE6+Fk4SXXmlSR5ccYXnbfmTwHX0dCpO1arur+vWNZ/9vn2lCy/0fT+BeP5599dPPeX+esEC6dprpXffdfa4yc425ycHT/PCRbKePc33v6f5qTx9B51ySmjimDCh6PFls0nJyeb4mzLFDPNVVvlyPeCtjLcew8eOmc+XJzW8DCvpjWP+PG8xZfnQYzAcgn2tNX68Sd7PmBGc7TkS7YUtWOB5SFJvmjQpfTwAiSUAYVPcBJ033WTGxy98I1jWFNfqzjHWuWSGkHDcEGdnm6EkatY0LQwdmjQx3eEdmjc3F/1vvSV9+WWwowaA4JswIfwTNj/8cOAJj1NOMQmYSNWypfv3hCS9/rr0559mqJ4VK0xF2IYNUv/+5v0pU8zwQykppgXkgQPOoTxuv1365Rf3yY8vv9y8v2CBs9L+hBOktm2LxuPvTXtqqvn/eJs0Phrdd5/07LNWRxE7/Klk9lRpmpbmfN62rekhUlIS9Y03TEXstm2+79shkHUi3ZVXSvv2OYdOGzTImmGdc3Kcz202M+fW2rXmueN19+5mCK7C/4epU813wYEDZo4gV+vWOZ8vWmTmDinOvfc6e0slJprh1mbNCuz3+eor955zp59uesSccEJg2/OXo4fJSSeZn9WqmWHyXOc9CnbPg4ULpX79zPBehY0fL735pu+9CQOVXMzQmZ9/bhpJDB9uvpu6dw9tHOHiz3fz0qWml5brOh07Bj8mb+6/3ySCHb3CrGx8EW6B/q5nnGHqEAonTV3FxZmeh56GIHzllcD227atGZqv8Fxarr3ONm0ywyjGqkmTTNLOlb/nsJNPNt+xx4+bc+Do0dLHH0sffGDmVfLV6tXm3Fpc4wDAHwlWBwAgttWqZW48JFMBdfy4uUj54gvz+vffTQWig2PCz4ULzcV6YcuXmwuQRx91toaMVocOmZa2hw+b13v2lLxOUpLnC8krrjAtLb/7zlwo1K5tWhcePlz0pujnn00LL8e4yw0aSDt2BPxrAEDQ3Xef9McfJrnhi4ULTZL9zDPN6127TM+BH3/0bf133zUVdSXdqPfvb4Z2q1DBJGYuzDHLW7f2f4itunWlnTvdlzVrZioQg+2LL4recPbr53zes6cZusebwkMI1ahhGoa8+aZ5PXeu873hw8130LFjUkKAdxtDhjgrMPbudSarKlSQDh4sfr1u3aJnYuhLLzW9XOAuMVH6J4Ak7yOPmIp21zle/LV6tWlV/OijvlXcnX22e4LVV1WqRMck4IHwZ3gyf7RvL33yiW9lR40ylc2OXj6F/5eNG5vzfnFsNuc5Lz/fDLnYq5f7efD8882jTRvnNfWXX5rhPm+7zfN2Ax2uq1Yt99eOv/G8eZ57bWzaJLVo4XzdtatJlHq63m/cuOT9N29u7h8c86NJzt9l/XppzRrfenF5Uq6c5+UXXOBfRWkwuB67H3zg/p5rMtqRYCvLWrZ09tI66SRznRHMHmSOea+8qVTJJILhmzlzSk4kTJxoflatas5jrr1258wxQ9hecYX7NV9hro1iXbVqVXTZnXeaOhDHseNtGMVYEBdn7k1mzDAJ+7Zti++B5GrsWFNvdM89zu+zvn2L71lWktNPj73GWrAOiSUAIbV5s2mN62jpZrN5vwD86Sfpt9/MBaqnxJJj0tXHHjOt1UJ5U/7UUyYxc801ga3foYP00Ufuy1wrDcuVM63d+vc3Fwm+3Gx6Gq9cMgknTxVpnlra1axZtAIxP99cSG7aZN1EnwDgi9dfd/aqcXBUPv3f/5lem7Vrm6RN4crEbdtMK/SWLd0THr60Nv7mG/Od49jmeec5E0veKlK3bjWt9l17jDZsaHoHFR7iZ94859joI0aYm/d165w3+pHG2/eWzRZ4UkmSXn7Z9Cbo1q34/fTrZ3qMPPmkdNllZpm3idWjXSy3xk5IlI6WchuOSpfSKK6yJT1d2nOs6PJAhbvC3GrBOHaffdbMn/Tcc76VL+2x4FCrlvd7l9xc6cQTzfMWLUyjOV+u6R1zqZY0qftTT7n3pnNV3FCQWVnOyn7JJH4kz/8HX4c2LW7Y7Q4dSjcE5Ny55js9EkauiIszwxZ6+v4qzfdZrCjuc/zBB9Jnn3nvweevWE8whNvhw957xxw8aOoACvc6u/VWk5B+7rmiw/UXp1w534fpT0/33nsqFp1wgmm84uBLo7KHHir57w5YhaHwAIRU5crmhikjw7fyGRm+twI78UT/bmSKm2zVm6uvLrpsyBBp3Lji19m1yySU1q83F2KPPGKG+vPUtbt9e5PUcZ3E1goZGdILL5iE0++/S0dLW7sDAKXkegOcm+t83q+f1K6d8/Vnnzmfn3GG+7Aarux2kxhq1crzMFgOX35pxoJ/7z3nMEMPPWRadbtWqsTHS1f/2/DAUeG0Zo2pJHOdM6JJE1O559rLdto00/J78WL3fbtWRj73nNSpk2kd6qpylaLzPPgrWobtufVW0/rf1bnnmp9Nm5oko91uev44MOdgdEovpuI8UpQ2MVJ4OOjrry/d9qJNMBJLzZt7n//IKg0bmkpZx1Cq3pJKjt5tGRmmktCX42Ds2MDiGjq06LLsbPPTMd9JJGjcWNqyxQzpFAhHo8NgSUx0TyLl5pr/14oVwd1PNPB1brRKlbw3AoH1ShpyrXx5c81Z+H94550m8eN6Dp8yxVyH33hj8ef2ypVNfUheHsdFSTz15AKiCR9xAFHNtdVi4ZZkbdqYn46Jc+vVC3z+DFdTphQ/bEOzZqZC8dRTzev69U2Pp4ceKr7llT8328X1WAqmKlW8V7oCKFvCcUPoqYdqaqqZj+L++6WBA00yxTE0zfjxznKOc70njoly/Wm53qKFtHKlSej8+KM57xZX+Veu0PB3XbuauYY89RZ1zDni6rzzpHfeMd8Vy5d73kf58u6v42zFfwf5ytsQUOEyd67pZbR+vRl33ldz5pgkXeFeusOGmZ9XXGFavhfXuvzPP529m6yWmlq015o3oZokPSJY1BvL9dwweXJwtunaA2X3bjP/wdlnm8ZEkjm/OCbMLm7+UXjWsKHpXe/rMKnhUr68b9fzjRqZBmjffedcVqlS8OLIzHQm46+7zlRY3nWX8/1XXzVzAr34onNZ/frB278VQt2YYOBA8z8LtDdONPcySEx0DjEMOFSpYnrdP/igcxg3R9La1amnBpY0+eYbM/yoL2K5JzcQLejQCyCqZWaaSj+73VxYLFlihpZITTVzRhS+WbPZTNkvvzTvvfOOqQT0h91uKrAcE/JKppX6nXea3kkAEEu6dzdDzIXSggUmgfXCC+7LXStoXVttX3CBGTbVW1JJMgmL0aOLzk8RSc46yznvxfr1Rd8v3MrUVkyib/wE6YEvPb8nBbfy0sFRYVDcePreuH73duggrVrlHD7WMXyTJxUrek7SLV4s5eSYpNvMmaYyNTGxaLn0dJOsPHDAPB840FwvWCE+XurTx/fy/pSFbxwNgSQz52QwZGY6n1ev7mxYtHat6aXuOlxjIPMzRarzzw/PfrKyTO+Iu+8Oz/6CzbVHq2QqaJ96Slq2zFSo+qNOHffXmzc7G4OkpRUd3rp6denaa83z//s/M1SrY/hVFK80ldfR3lvDdRhG1+9UR49uWCPSEirTpklnvVBiMZ80bmwe/tbRRKvCDciAaBMVX3M7duzQmDFj1LBhQ5UrV06NGjXS1KlTdeTIEatDAxBG3rpwOy6uBg82w8oNHuy9Eq1lS3Njf9llpuLSnxt7m006+WTpllvM6woVTCu2lStNjyUAKCuCMYznOeeYnyUN01HYtdcWHSbNEyuSSgsXmu8G1+HvXBV3E9mypfmZ4tITytv8TQ6VKkkn1PZe5q67TAXiM8+UvD1fVawo7d1rJnUvLdeeso6/gz9shXpyeZsPo2JFk8x87DEzJNTrr/u/v2Dxp9LRlwme4b+ePc0xEerEXWJibM8BVq2ab+Ueftjz8kirKA2XJk2ke+8NrOdN7drSHXeY5wsWmPOor3/HM84wPTwBbx580CRz5841x9a+fdIff5S+5zRCr6yeU0srPd0kTgPtDdm6tX/lW7QIbD9ApIiKxNJXX32l48eP64knntCmTZs0a9YszZ07V1MirQ88/OKpFSngTUpKyWUCMWuW56FISqrYmj7d9JDaujU0cQGAVRYu9K2ctwnNfRWLN74dO5rKl8I9a2bMkAYMcM4TVFhqqrRnj6m0cSjcut0TX3rLVq9uhhK8+OKSy/qjUiXPQ//568QTS7+NQPXubSpZR4+W3n/fujhKEu0t3yPV8uVmfslQ9OorS3yd/8ifYZ0dwwfCcJwDXIcnu+02M9S3pyFly4L+/c3PypWtjSMWeLoeq1/fDD/p6D1SsWLk/q1j8XoS4RcXZ0YS2LkzsPUffdS/8v36BbYfIFJExVB4vXv3Vu/evQten3jiifr66681Z84c3X///RZGhtLo2DGyb95R9iQkSEePOl937Oi9fHy8NGhQaGMCgFDo1k26+mrPk3s3b26GmvOlkiqYQ6EMG2Z608RKi35PSQBf5hJyHXbGoVMn6cNtUuH62OrVpaOVTaJq3qZAoowcs2aZSqExY8K/74SE0A/3iMhlswXW4M1br7iyyNt8YYFW+DZvLn3ySWDrRhtfEm7ffWfunwsPO1iWK9SHDDFDqQbS0xUAPCnN97vrCAyerukLa9o08H0BkSBq273t3btXVapU8Vrm8OHD2rdvX8Fj//79YYouuvjbVTNY7r7bfQibRYvMz/vu868lG8qOHj2sjsB6fDaA6BYp42i/+66pjPnqKzM3zT//ON875RT3ssXd8AS798RZZ5n5IL70Mk9QWfX++9KnnxZd3rq1tHt3bPS0qFZNeu456fTTrY7Ef7728Cst1+ECETvKclKgJLNmSVdd5Zx/rSSBDl0ULerXly66iKSmK5vNNJbxdShGALEj0utGuG5DWRCViaVvv/1WjzzyiK4oYVDgGTNmKC0treCRlZUVpgijS26uNfutUMEMJeZw/vnSkSPShAnWxIPIN3++1REYkX4BA8Aavky6HSkViI44mjaVzjvPVFJt2iRNmeKc/2LtWtOrqbgJ0v2dZNzVrl2el7dq5ducQmVNXFzxw81RwVg8xxBJoVbaJKtrz78mTTyX6dTJv/kgEXrBOp//8ktwtmOla6/1vaw/19FVqpi50E491bfyVOIBKAs6d7Y6grLJ33qgSLnvA0LJ0sTStGnTZLPZvD42bNjgts5PP/2k3r17a+jQoRo7dqzX7U+ePFl79+4teGzevDmUv07UatjQmv3a7aZ10YABzqFhmHcJ3oS6RXa3bqHdvsTFBRCrXn5Zatw4csed90VWlvSf/5hJayVTkf3oo8UP41B4Tpznn/c8tJ4nvswbBJRW4STNxo3WxOGPM84ouqxVK5Po5To5NlWvbnUEpVfSscn1LwAEz6hRVkcQfi+8YHUEoRPN94+ApYmla665Rlu2bPH6aOkyWO5PP/2k7t27Kzs7W08++WSJ209OTlalSpUKHhVpAhs2l1ziW7m4OOnVV81k1oDVXKZyAwCfDRxohpVzPPcmlivXOnaUXnpJatbMezmGq0G4uDaeqlQpdMM/l7Yns+M6+PLLJU/Tx772Wum2j/A6/3xzvDm+F8qCSy/1vSw9/z3j7xL7cnOlWrWsjgKxINjDUUeDGjWsjsDdK6+EdvsXX+x8npIS2n0BpWHp6ahatWpq1qyZ10fKv5+gXbt2qVu3bmrbtq3mz5+vuLJ4Jg2jb78t3fpcMJVdF11kdQSBi+UKXwDhUdbOI67DsDnmtsjLk04+ueR1HeXPOSfoYQGSTKImGowYYYaHnDNHSk2VbrtNqlvX+X69etbFBv9Vriz9/rtJtJcFnTqV3KCgrH03Ap5kZkqPP+57+UGDQheLJ9H+OY32+IONv0dohXK45ebNpWeekX79VfrjD4Z5RWSLiuzMTz/9pG7duqlu3bq6//779euvv+rnn3/Wzz//bHVoMenjj4sOb+MwbZrz+UMPeW5ZVZo5FxD9Iq0lSaDq1LE6AgCIfHFx0o4d0tatzrmRkpN9GxJv0yZp5Upp5MiQhlgsR1LMtQIfsSWaho6rXdtZCXTHHWZoSUSvhISyU6nn71DVrVqFJo5Y4hiCtn59a+NAcDgqhU880fRs//FHM7zpoUNSdnbx6z35pHTTTeGJEYB1nn3W/Jw40QxLvnKleV2tGsPkIfJFxXS/K1as0LZt27Rt2zbVKVTba6fPeFANGya1b190+fPPO3ui3Hqr54x5167SmjW+74t/HSKNawXAbbdZF4c3fG6A2HflldJ//1tyQ41PP5XatQtPTN54qviaOFE6eFCaObPoe45hR6tWlXr0CG1s3nz8sXTnndLdd1sXAyLTV1+ZedNCeS3wyCOh2zYQTv4m0DIySr/P4hpBWjV3cDC4XuOvXWuGyLz9duviQfDs2ycdPSqVK2den3CCeUjmeq9CBfP8scekbdukWbOkt98210n33GMSjatXOyubXS1dKr34orR4cVh+FQB+8PX7sX9/6a+/GPIO0SkqeiyNHj1adrvd4wPhceGFzueFk0pr1phhbIprWfnmm6GLCwgVf8aKB4BgeeopM0yKLyP+VqggPfpo6GOS/J+bJjlZGjfO83uPPVbqcIKiTRsz30FJQzghujkqoPv08X2dpk2lSZNCE49khkm75prQbR8IJyt6ZjVqJC1fLn32mfvyBQvCH0sotGhhJqrPzLQ6EgRD+fLF9+wrX1765x/Ti/vKK6UHH5QOHHCf+3fKFGnFCtPg4d13pQ8/lG68UdqzRzr3XGnRonD8FohmVJ2GT6DfiSSVEK2iIrEE63k7OXbtKi1bVvz47/7cyCO6zZ5tdQTB4+2YZ+J5AL6YNMm0NnVVUhJj7Fjft5+ZKV19tX8xPfecaQ3rr9dfLz5R5IvPP5cGDDAtc/0dNgkojffeM8M3P/GEf+uFcjrXKlVCt20gErleVwdrrohevUwDAVcMHYdolJAgZWU5PyeOHkyFDRkide8udexoElCOIRMBT8rCcKyR+DsW/l4CYh2JJYTEVVeZC6QRIyLzZI/QuOqq2G8Ns3gxk2gD8M2MGdLu3SahIpmEyrx5vq2bleX9/S5dfP9+7drV+XzECNPS21916kgPPOD/eg61akmvviqdeWbg2wACccIJ0vXX+18BFxcnzZ3rW9lQXOt26CBVr+59/g1Yi3sc39lsppfe8OFS48ZWRwMAiDbR8p0brMYTQLQgsYSQOOEEMxnlc89ZHQnCKSHIs7ZNnx7c7QXDeeeVbv3mzYMTB4DoEBdnhlvbsUPau9f3uSXmzg3eBOfB7CHkmFz26aeDt00gUl1+eel66pVGSoq0a5eZawWRKdYbUwXbI4+YoeqKqxyMlkpDIFZMmGB1BJ5Vr251BEBw8L2GsoDEEtwEc4ivxMTgbQtl0223SZddZnUUwTV3rhnqav36wNaP5kmJgbIqPt45PI+378a2bZ3Pa9TwvbdEOI0caSaXHTOm5LKuCS2GvwufOFpKBtWUKWZYkwcfNL3uunQp3fb8SVQlJlIpgchHgg2ITldd5f46Ur5vIiUOoLQ4llEWBLl/AaLVyy+bVsh33ml1JICTzSZNm2Ymsx840OpogqN6dfP7+Ou//5VWroy9RBsQC/y5aUhPN+c1u929V2bXrtKSJcGOzAj2TY2vk8smJ0vffmt+1+Tk4MYAhEvVqtJnnzlf9+9vkkvr1vm/ra+/NnOjAbGExFJwlCtndQSIZps2SS1aWB0FAKCsoccSJJmJIF9/ncmEEXkyMkzr+FdesToSa515pjRzJj0BgVgwdapJLjmUKyetWRPcXsOR4sQTA5vTCYhUNpsZns7fyvRbbiGphOI1a2Z1BLDaE0+Y+RVzcqyOBNEoK8tcXwIAEE4klgCEzFlnBWc7ycl0IwZQ9rRrZ+ZlK1/evD7jDM/lmjRxf33KKaGNC4A7EkYorexsqyMIHNfowdG4sel1MmqU1ZEgWk2caHUEQOhEwncNPXSBokgsAQgZXyepB4BoFqqbjMRE6csvpQMHpF27pBUrnO+5Di23dq3Ut6953quXNGhQaOIB4O6LL8zn0rW3ySWXmM8kYl9WltURAIBThQpWRwCUHZGQ6AIiAYklACHDly0AlE5cnDmX1q4txcdL8+ZJTZtKc+c6y1SvLr35prRli/TGG+7rh2reJgBSy5ZSz57uy269VerUyZp4EF5jxwZvW7SCBhAqkyZZHQFQNlEfhrKAxBKAkOGLFACC65JLpK++MkPmFNasWdF52Oi9VPbw1RuZWra0OgIEW3y81REAgLvPPnN/3bq1NGNG0XLt20sNGrgv4949tvD/DD7+pkBRJJYAAABKIdCbjPnzzZB2r74a3Hh69TI/k5LMT26Cyhj+3xHlnnukxx+XzjvP6kgQzc45xwy9l5pqdSRF8R0DRI42bUou89ln0kcf8dlFdOF4BSITiSUAIRNtX/6DB5ufrVpZGweAsmH0aOngwaJDaZVWu3bS559L+fnmdbSdi4FY0ratdOWVfA5ROsuWSZs2Sfv3S3v2WB1NaPAZAcKjTRs+b2WB6/+Y4VYBhEqC1QEAQKSoV0/688/IbA0KIDaFaiilk04KzXYBANZKS7M6gtCgohuAxLkAsYNjGWUBPZYAhEw0fpGmp0sJpNwBBOicc8zPypWtjQMAAABlG9ejkGK3x1I01jcBsYbEEoCQ4YseQFnTr5/0wQfSN99YHQmAcIlzuaNKTrYuDoROQmLRZV27hj+OSMT1PhBZXL+T5s2zLg4AQOyjXT4AAECQ2GxS585WRwEgnJKTpZkzpUOHpNq13d9r1MiamBBcCf8OW/ryy1L3LOnnn6UWLayNKVLEakt4IFqNHi0984x53rCh+3v16oU9HABADCOxBCBkaMEIoCyI9HNdpMcHxIKbb3Z//eWX0q+/Fq3UQ3Rr0kSqWtU8gonzNIBgeeABM7z7hRc6lz38sDRxovTWW8Wvx3mobBk+XFq4UBowwOpIAEQzhsJDsTIyQrt9WrfFPi5OAQAIr3LlrI4AkunN0q2b1VEAAMqa9HSTXGrb1rns2mulv/+O/Z6W77xjdQTR46mnpNxcacECqyOJXdSHoSwgsYRinXmm1REAAADAH00zrY4AQLj06mV1BAAQOU480eoIokf58tLAgVJqqtWR+MY1UQogcpBYAhBUrj3RaKEBRK6+fb2/X6eO9/ftdnqeApEoLt7qCACEy+DBZr6nw4elN9+0Ohpr1apldQQArNakidURIBT++MP0xIs21IfFvvfee0/9+/dX7dq1ZbPZtGzZMq/lR48eLZvNVuTRwqU7aU5Ojscyf//9d4h/m8CQWAIQMtdea3UECJZ27ayOAMFWUgXUjBn+b9Nmk/bvDyweAADgm08/lR58UBozRqpZU0pKMg1Gbr/dWeaHH6R9+5wNQY4elR59NPixRErF2SOPWB0BACAUKle2OgKDRpUo7ODBg2rVqpUe9fECa/bs2crPzy947Ny5U1WqVNHQoUPdylWqVMmtXH5+vlJSUkLxK5RagtUBoGypVcu0qpM4KZcFWVlWR4Bg6dvXzLvWrp3011/SvfdaHRGCoVo16bffPL930UVmOInhw6Xvv/dte7Nnm+EU7PbIqWgC/wsAiCYdOkjZ2dJDDxVfpm1bz8MCTZ8u9eljrtnq1nV/Lz5euvpq6ZprghpuxAxPFE9vTQBAmHB/BUnq06eP+vTp43P5tLQ0paWlFbxetmyZ/vzzT1188cVu5Ww2m2pFSVdseiwhrG680eoIAAQiIUF6/XVp2jTpnntMr5Q//rA6qrKrdm1pxYrSb+eNN0zyaOlSaeXKou936iTt2OH79lzHNf/wQ+mWW0odIoKAGx8AiB7r10uzZgW+fseOUv36xb9fpUrg2/YkLsg1Cq+/LlWqZK5NAITPySdbHQGCiet/6/E/iF779+/Xvn37Ch6HDx8OyX7mzZunHj16qH6hC7cDBw6ofv36qlOnjvr166eNGzeGZP/BQGIJYdWqldURAAiGaJnkMxocOOB72ZdfNr2BfvxR6tlT+ugj39Yrbr6kDh2kb7+Vzj1XOvNM5/IePdzLzZ1rhts5+2zv+3Gdt6ljR+muu6T//te3GK+4wrdykYibBgBAtKhe3fk82EkhXzVuXPy++/WT/vzTXJv4I1KGSgKiVYSOsoQAud6fMFoQ4J+srKyC3kVpaWmaEcg8ASXIz8/X22+/rbFjx7otb9asmXJycvTaa69p0aJFSklJUefOnfXNN98EPYZgILGEsPjiC2nhQqlXL6sjAYDIUqGCb+dGu10aMsQ8d9wonHqqtGpVyev27lPyzaLNJp10knleuDfU5ZdLTz9dcitnTwkW14SVN48/Lp13nm9lAQBAYFy/q7OzrYlhyxbvDWv8SXjNny9NnCidfnrp4wIQ/fLzrd2/3S799JN0ww3S6NHWxuJAYgnwz+bNm7V3796Cx+TJk4O+j5ycHKWnp+vcQi1pOnbsqIsuukitWrXSaaedppdeekmZmZl6JEInkySxhLBo2VK64ILSter2tdU7EGz0zoEVOnTwrVy3bqZ1b2EDB0r9+5vn8XG+DW/x+efWzI+Ul2f2uXixmccLAADEroQEqVy54Gxr9Ggz9ye9hwFIZl7vlSulpk2lDz4IbF65Y8ekmTOl1asDiyEjwwxpOn9+YOsDsFbFihVVqVKlgkdycnJQt2+32/XMM89oxIgRSkpK8lo2Li5O7du3p8cSok+kXZz72uodCLby5a2OwHqRdj4oC957L7D1Ro40Q9Dl5krt2wc3pkDnZaha1fv7DJMKBE+N6iWXAQBfHDxodQQA4L8ePaSvvpI6d5YeecQ5tGbLlr6tHxcn3Xyz6Ql5553u7/k7fPegQc7nN93k37rwjjoKRKs1a9Zo27ZtGjNmTIll7Xa78vLylJGREYbI/EdiCQAQNB9+KD38sLRnj3ldr57phQL/JSWZv5+/nn1WuuWW4McjSbffbuZ2eu45/9YroREOgCBKS5cieH5XAD7q0sW6ff/5p3TkiG+Nq4I1xBIVhABCJTfXDL/5xRf+r3vrrc6RJN56y8w7WxxP89rm5Jh55e65xzy2bvU/BkQnvtdi34EDB5SXl6e8vDxJ0vbt25WXl6cffvhBkjR58mSNHDmyyHrz5s1Thw4d1NJDtnv69Olavny5vvvuO+Xl5WnMmDHKy8vTFRE6KTWJJUSVu+92f12zZsnr1KoVmlgAFNWxo3TttVJamrR/v7Rtm5k359tv3ctdc437hfeTTxa/zfvuC02skWzYsOBvs3Pn0m+jShUz/9KIEUXfq1Ch+PVq/Huu9mXYG9dWfdEi0m8aIj0+BF/r1lZHAMAfns7TVt7DpKdLiYnmeeH7L0maOjWs4QBAqdhs3u9VSrJ+vUmi9+kjnXJK8UOMe5qGpWJF6ZtvnL2VmjSRDh82w+25mjlTevBB92WM2gNEtg0bNqhNmzZq06aNJGncuHFq06aNbr/9dklSfn5+QZLJYe/evVqyZEmxvZX27Nmjyy67TM2bN1evXr20a9cuvffeezr11FND+8sEiMQSokrhL+rXXzdfyK4TvheuhPa1uzNQnLZtrY4gOqWmOislTjxR+vhjacAA8xl96CHnZ7VqVenSSz0P/ZafL914Y9EhCCTp11+lNWucrwv3DH7zzaD8GmF36aX+j8d9223OdYvTq1fgMXmzfr0ZJsLb0H1J/x4HL79c8vZuvDE4cQEAAP/9+qv768mTTYXqkSNmeKlbbpGmTbMkNAAIiTff9G9eZU89kyTfh9BPSjLD7TlceaUZeu/GG02PUYfGjX2PCUD4devWTXa7vcgjJydHkpSTk6PVhSZrS0tL06FDh3RpMZU3s2bN0vfff6/Dhw9r9+7dWr58ubKzs0P8mwSOxBKKFektnHv0MPOHxMVJCxc6l192mfN5tWrSo49KKSnhjw+xIydHuu466csvrY4kurVvL736qjRhghQfbxLC775rxr+WpNNOk9atM621JGnLFtNaNz7eDEFw4YXu26tWTeraVdq1S3rnHfPTbjfrbd4s9e0bvOFZwql/f+dNySWXmJ8dO3pf59RTzfAOTzwR2tg86dDBTGwbrARsuXLmGOnaNTjbC4doPM4AAHD10ktmuKhq1Ty/n5gorVxp5nEEgFAJ93V1Soq5b7z++vDuVzIJ+3XrzFDyDunp0s8/S99/b0YBAYBIRmIJMSEuznQn3rdPqlTJVHL27i1t2CA1bSodOuS9NX1hn33mPsTDjh3SJ5+ULkZ/9o/IUrOmNHu21KKF1ZHEFptN6t7dvQIjO9u01rLbpWbN3Ms/+6znyozataWzznImw5s1k5o3D13codatm/P5LbeYoedWrCh5vQoVvDcIaN/e+XzGjIDDC4v77pPmzLE6CgAAYk9x1wpDh0oDB4Y3FgCwUq9eZvQFyfQWathQmjIlfPtPTDT3vwkJ7str1gxsrl0ACLeEkosA0SEpyTlB/Omnm4eDzWZ6Q7Rp4z6h9aRJZj4PR6+IvDwzbq7NZobQO/lk02q+WjWpfn3p3nulr7+W5s1z3/ecOeZGLD3dDB9Ru7YZO/e778x4uo4uzD/+aHq/dO1q4lm92lSsAyhZfLxJtLRpE9tDXFas6HyekCD17Bmc7VatKv32m+kN5ctcR1YrPLRhJIv0Hr4AAAAAnBYtks4/3/m6alUzLzDX9QgWjiWUBfRYQpny6afuXZxnzDCt+OvWNcmgvn2dJ//ERJN0cu1NMXGi9PTT5uHqiitMq5LkZDPeblycuSix293HxT3hBFMxftpp5rVrzwQrPPWU++usrOJ7ZjVvLk2fLn3+eejjArzp2zf2WnAlJ4dnP1WrRkdSSXLOz4XSGz/e/Bw82No4AAC+c1R4lpU5NqiAA8LvhBOsjsA6nuZD8vU8FA3nq2iIEUD0I7GEMsVmM8NsSabXQ6Auvtj5fNu20sW0Z48Z03z58tJtx2HcODP0nycjR0rHj5teVZ9+Ko0dK+3cKT35pFln0ybplFPc17n5ZjPE4ObN0u23SyedJI0aFZxYgXDIzbU6gpLNni1lZkpz51odSeQIV7KtLMjOln7/XXr5ZasjAQD4atAg0+Drs89Ctw/HfdGQIaHbB4DIVdI8rlYIdUJkxgxzfj377NDuJ5IwH2xw8HcEimIoPEStQE/qGRkmUeKphYqv4uKkgwdNkiY1NfDtSGZCxqFDzfMHHnC2LJfM7+jtwmrsWOmCC0wPqJQUE8911xXfI2HyZLO9atWcPbHq1JEuvdS9XOvWZljAm2+WZs4M9DcDIkM0tNZq2NAMswmnxEST0D52zCS0UTpVqlgdAQDAHzZb0QZfwda/v7RrlxkWHADKgkmTrI4AscCXOgbX0Y+AWEWPJZRJFSua+VpKo3z50ieVChs3TrrsMvN8xAj39ypXltq1k/Lzpf/+V/rjDzOU3RlnmArYPXuk7783c0FJ0l13meH5PvvM9FQaPlxq2tS3ODZuNImz4pJK06YF8tshmkVDcsYXV1xhdQTwR/PmsT2fFgAAVqtd2zSaAwDEjli5f49Gb70ldepk5vECYh2XkLAM3Ug9e/xxM0zd/Pnuyzt0kDZsMC0KzzzTJJpcVazoPu/MLbeYJFSbNtKzz0oLFvh3ceGtR1eDBsXPxQREskcftToCBIJh8QAACI5LLrE6AgCITZFaxxWpccWqPn2ktWt9b9gNRDMSS0CEiY+X2rYtfY8qKbStVE45xfO4xFy0lB3R2AoqGJ8rhJ9jAnMAAFA6detaHQEAwBfUrQCIdCSWAABARCMhCABAZKLiEwBghWhs6ArEGhJLAICYRoUHQo2bGgBAaUTz9wjXWQAAAGUTiSVErJQUqyNASaL5JhgAAAAAAMAfJNQBwCCxhIhF0gJALDr5ZKsjAAAAkYRKSgBW4NwT2agTAxDpSCwBEW7gQPNzwgRr4wAQHKmpVkcQfRISrI4AAAAAiC0kloIrkhJBkRRLrOBvChRFVQ0Q4V55Rfr1V6lmTasjKYovVgDhUK6c1REAAAAAQPQhgQggVOixBES4uLjITCpJJJYAAAAAAAAAoKwhsQQAAMJm2TKrIwAAAJGgdWurIwAAAECgSCwhYpXUXZfuvAAQfc45x5y///tfqyMBAABWatHC6ggAAAAQKBJLsAyJIQAou04/XTrlFOmCC6zZ/8yZUpMmztdNm1oTBwAAAAAgslGHCRRFYgkRi/l7AITC8OHm58CB1uyf1rlGQoL0ySfSwoUlly1Xzvn87LPNz+eeK93+O3SQ1q51vn7/fal9e/cyV18tnXZa6fYDAAAAAOFEfVpo8fcFDBJLAIASXXWV1REEz1NPmXl+nn/emv03aGDNfqPZTTdJbdpI990nvf66tGePNGKEtG2beR4snTs7n3fvLj36qJSbKz38sPTbb9KKFcHbFwAAAACEGj1tAIQKiSUAgFfVq0tVqlgdRfCUL2/m+alQQVqzRmrY0OqIUJLKlaXPPpMmTDCtw9LSzPJGjZzPC8vM9H8/d93lfF69uvlZrZp07bVS1apSz57+bxMAgFgWrApLWn8DiHYkcMJr0CCrIwBAYgkA4FW03+inphb/XteuJsmE2BMXJ02ZUvz7J59skowO5cqZZOPcuVJWlukd5U21as7n0f4ZAQBYq6x+j1AJCyAace6y3oUXSjVrWh0FgKhJLA0YMED16tVTSkqKMjIyNGLECP30009WhxXTyuoNDgB30X4uOPNM6aKLpHvvLblscb1fPPnzz8BjQnA9/LD5Wb68M5k0e7Y0bpyz51FhVaqYRNKrr5qhER0JyMsvlzZtkurV877PnJxgRA4AQNlF5SwAIBAJCVZHAECKosRS9+7d9dJLL+nrr7/WkiVL9O2332rIkCFWhwUAiHBxcWY+pYkTSy770ku+bzc9veQyQ4d6f79HD9/3h+JddZX5323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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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q166dZs+ebXkMViCxBAAJYOtWacqU8sft2hkXnhs2NIYeSE01xgGeNcu4GPvKK9KiRUYL5d27/Y+Dfe+95fdDHe7l+uuNg/HVq72f95VYcrm851sI5aLl6acbww55hldyst/9zjixOeec0OuorgWwv1byZlvB22HjRiP5mZVlPP7oo/LXzjvPSCjl5ZU/52kN6XIZQ0elphoJpK1bjcee4S0++cTYtnJzjblP/vUvo+fXvn1GQsjD7NBpyclVnyssNE6yu3cvH8Lr9NOtm59s4UIjYfPoo8bnc/So0crX08ux4om/VS3/Ro6U3gpybpOKJ/hjxxrJjsOHjc/+mmuM58wMRX3uuUbCpKJvv/VOSp9yivH7ee65xv9+8WIjkTd8ePnF1+bNjeSV5wJAdS3E/V3oS0mRvvjC+B+cfbbxG5eTY1w4Ly42/heeobqSk425dU45pfrtdOTI8u+7ZNS9cKGRoNq5U7r6amPemq1bpe++M4aX+flnY165vn2NBNf555evp+Jn63IZ35e8PGMOpN//3hhmEd7MXGSqWdOYG2nAgMjHE8iWLUYy5Oqrjd+beBgWTTLm77vxxurLnXWWcYzzySdVX6td29gGTM7BXCbQNlinTvz13jvjDLsjAGJP167Vl3Giij2PKrKil+DJJxs9TP2to7JAjTTy8ryTh4895v16OA0jKh9nexrubNtmNNqaPr38PZx5pnHMZoVI9MR0UvIoGCefXH2ZRHPppXZHELtq166trKyssluaxS0Tnn/+eY0ZM0aLFy/W2YEmn5ZUo0YNnXrqqfRYAgL597/tjsB6d95pdwRAuebNvRMp6enGxe7t28ufa9LEGDaqUSNjuIJhw4wW+XXr+r/I1bHCUE4m5yusIjXVOBg/80zj8eefGy0n+/at2qq4bl2jt8WxY8ZE3FbPOxRPArXY8zBzkvbww8bfihOa9+/vXeb1170fV+xp0aCB0WPiiy+8y8TyheW2bb0ft24t/fKLcQG1uot/TzxhlK2YeHjmGSORdOKJ3mV79/adeKvcQyqW1Kpl9FryJK1q1DBOah95xEg6PPmk9PXXxvut+PvhT+WegQ8+aAxnctttxv3TTqs6rJvHt9+WJ7SaN/een23SpPJyLpfRitUzFMoTTxhx+lNx6JXHHjMSJhW1ahW4VXONGtYl8iryxNG+fXnPsYpSU8Mb0m/zZuMz9/Ruu/RS47Nv2NDomXLaacb/vUkTIymdnm5s3ytXBk7iu1zG9rBjh7nJqd9915nHfU5So4bz5yIYNqzqb7ZHkybG0Eu+jB5dtQdgrUzvxK2vfe8JJxi/XfEyFJfZi7xAIgllTkVYq2NH6dZb/Q/VXbG35bBh5ffvvdc43ww2uXDppUbjipEjy5/7+OPy+82alZ+vfvmlMRLHX/4SH8PzOR3Do1aPz8geCxcu1KhRo7RgwQKdZ6K7sNvtVklJifIqtnyNISSWEBOCGYIrXkydGhsTQgP+1KwZ3FAn4c7xY1ZBQXlX9crz23gu3LhcxtBu1V34iMYQXtH2zDNG4mfiRO8Ez/jxodU3bpwxlNUnnxgX5Nu2lSr3sj7nHO/EU2WnnGJuqJVoCLWXelqaMeSTmd/tygfhLpe5Ya48GjUyEhpFRYHLVRyWzO4TVJfLSO5I5fOzmF1uy5byx9dea4yjf//9RgvV99/3nyRt1croCbNli3cdofAcs2dkGOucPNmYNLpVK+P5qVONvxMmGH+ffNL4TsyYEd56A6m4/b75ptGDIpJyc43PPBJJMcn8yWnXrqHPLxXPzA7TGe8iOZ9ZNHl+7yp7/PGqx0OtWpX3Vvzgg8DbghVzOAGwBxdhDXbvz2bMCDyPnkfF/9eVVxp/gz3WGj/eGA42JaW8cZOn53hlbdoYw9j6Goo11O9OxcZQ0ZCbG931AfHuwIEDKikpUcn/Wg5t3rxZJSUl2rZtmyRp4sSJGlkhM71w4UKNHDlSDzzwgLp27aqdO3dq586d2usZg17SPffco5UrV+rbb79VSUmJxowZo5KSEl133XVRfW9mkVhCFY0bR3d9l1wS3AW5eHL//XZHAFinpMRotb5wYfTWGW7PjjlzjAvKK1daE4/VKiYxPCdpW7d6l6l8IjJihDFUYeXPJtTEjssldeliJO2uucYYJs5XEjESY3xHgpkeE7HguuvKT3J9qV/fmKvniy+MHkK+hsWzm7+T5Ion+w0aGL229u41htULNO+JPy1ahH8xp08fo6fM1q3G/G9TpnifrE+aJH3zjdGaVTK2iYMHjVaxoapZ0/txz57ejytemDnrrKrl4SyVE6ihbAvx4N137Y7AGrVreyf3zzzT+A1LTTWGHx092v9y1XHqeQ8QTyoegzv19zjRZWaW3/fMJVN51ALJ6JX/yy/RiSlYkWgkGeiY+oSORu+ud96xfr3RRiK4enYniZ1g/fr1KiwsVOH/Ms633HKLCgsLdffdd0uSSktLy5JMkvTEE0/ot99+0x/+8Afl5eWV3W6sMJbznj17dM011+j4449X3759tWPHDq1du1annXZadN+cSTF4mQJ2W7/eGF7KTCuQcPTta1yw6dkzNi+YWeH6642Tz86d7Y4ECF9mZvzNEdCkifTSS3ZH4V+rVsbvYHZ2eeKmcivpYA747r3Xe4gwXydPTueUk4hVq4y/sdITzBd/PR4nTzbm2pGMBJnkPUyUXQLNjeByGQmnisIdCuq116QLLzSSWE2bGsc6b71V/nqin8x17+6MCxdm3Xmn9M9/lj9+/XWjl6i/BEU8aNBA2rWr/PH55zunx5JU9TfAs592uYxhmOb9NbR6zzlHWr7c6BV5OKwIAWs45dgpGCkVeoJbPHUGbJSaKumwkcBPTjbmC3S7ff+P//EPaeDA8t/2o0elpJ5VyyWSGq7QR3+INYn4uxasRD8XsUKvXr3kDvBBFlUaomR15YnFfXjooYf0kOdkOg7ESftj6S9/+YtOP/10ZWRkKMfMxBIIWV5edFp8p6UZySWnt9D1N347AGvcdpvdEYRnxgzpj3+0pq4bbii/P3my/zkiEPs6dLA7gurVrCnNm2cMDeVx8slGovSGG4z5kTyJpUR0xhnS7t3GZ3HhhcENPepUixeX358/37447FD59KVDB2No04rzH8aba67xfsxFHHOeekq64w5jCNpYFQ//S5IB1nFqI0+rxcN2EU2x+Hl4GgS8+abxt06dwMO7VRyRIZKjM9g9pLWXGPy/AYhfcZNYOnLkiIYMGaLf/e53docCi3TvbncE0cGBOmCdWDyBsVOgz+Oqq6xd1zPPGBfGZ840Vz4Weqj40qiR3RF4e+EFuyMIz6hRxrxJlc2aZcyPlOic+ptV8djG7HucO1caMqT8cYsW1sYU6yo3ZnRCOznP0EIITt260rRpUn6+3ZH4Fw+/XSbmu4ZJp55qdwTxIR62CxgCDTnapo3xt/IQxZEUamKJ71zo+OyA6IibS9733HOPpKrdyBC+d9+VunWL/nrbtYv+OgHEnx49pLff9v86B43lIvlZdO8u/fyz0RLwD3+ovrzViS2rPPKI3RF469/f7ggQLZ6h+Dy9uOJ5+IkmTaSxY405KszOTZFoiaREULnHCPtjRBON96wTL/Nowh7B/rbH+jC3X3xhzKlUcQ4mOA/HJEB0OPpw7PDhwzp8uHzk6v3799sYTewKNOdAJMXKMD9DhlRtMf5//ydt21Z1eKpmzaT775cuvTR68QGJ7tVXyyfDNjMpdiKrVUsaNkw6dMj4vbKa2TlnFi+OsSEfKrjoIrsjcLYePeyOIHbl5Eh79nhPGB7PnnzSXLmZM6XPP5fOOiuy8QAAqornRgyITdVdsD/99OjEEarkZP9Jpdq1JadfNSThAg/2D7CCo9umTJs2TdnZ2WW3goICu0PC/7z9trU9lsIZ2mjx4vIx/6+5xmi9MnKkdNdd0kcflZd78UUj2TRsWHixApE0YYLdEVgvM1OaPduY+Pz4462t+9VXra0vFixaJK1YEdmThuoOQmOtBeCiRcbfbt1i72TKKQf0GzdK990n3Xuv3ZHEtuzs8l4ent7isfadtNq4cdITTwT3Pp2YhHLKtg7AebKz7Y4AiA31G9gdgbdIHCM6/LCzjNOPr4FYYWtiacqUKXK5XAFv69evD7n+iRMnau/evWW3DRs2WBg9wmH1/EqPPSaVlvp+7fLLpYMHAy8/ZIj044/GhY+Kw3pUvIh92mnhxwlEmmeIJae5/npp6lS7o0C8GjpU+uwzadUquyNxrrZtpfHjA49pD28PPyxNmWIMyQJvTZvaHQEAJA723QAAIBS2DoV3/fXX69JqxhRr2bJlyPWnpaUprUKWYN++fSHXlYgKCqR4ycW53f57LT33nLk6cnOrPlezpjEfxy+/SA3CaL2SkWEMTQVEWiK2iE7E92y3eGsB5nLFzvCrgEd2tjR5st1RxCYn/q47sUdA5f9TvO0bACAY/MYBAODN1sRSvXr1VK9ePTtDQAAffih98410wglSz57hTcLYvXv4kzh26ya9+67v13wlhaxiZpL6yy6TFizw//pxxxmt5QFEx8sv2x0BYI4TL6AD4XLidtG6td0RRF4NRw+yDgCJyYn7ZH/IHToHieDqJdK2jciJm8P/bdu2qaSkRNu2bdPRo0dVUlKikpISHThwwO7QHCsjQ+rY0fhBbtUqvLqWLQtvvPx//1tat873azVqGIkvX3btClyvVa1H09OlRx+1pi4A4RswwFy5WD3g9PebBgCJoHZtuyOAGbG6DwWQmPLzzZet/7/2zeHM1ZyouBgdPRGZYylB9t0ZGXZHEPvYlmGFuEks3X333SosLNTkyZN14MABFRYWqrCwMKw5mGDeQw+Ft3z9+tJNN4W2bOvWRq8pX7p1k44e9d9CMlrzzVx4YeDXJ0yIThwAnMFsZ15ahwNwojFj7I4AZrAPAmJfIl04dAXxm+T5/erXP3EutFuNzw2xrJpZVwBYJG5OB4qKiuR2u6vcevXqZXdoCaFePWn37uCWGT7c+Nu3b3jrvvxy/6+Fm/CyynnnBX59xAhp06boxAIg/mVlmSt3wQVG4v2aayIbDyInkS74AGalp9sdAcwgsWStTp3sjgBIPMHkRhI1kZKo79uxEuT/mZpqdwRAYuB0AKbVrRtc+SeflJ5/XnrhBeOxv15HobjqKunIEalLF+vqDFV2trmDrUQYWx+ANaZPN1cuPd0YKvSJJyIbT2XxlAzhpAIAoiPcxmSJbsQIuyOIP+3a2R0BACdJTrE7AiByOna0OwI4EYklREytWtLQoeUt71u1ktavlzZvDq6egQN9P58S5E7/ggvK7598svF36NDg6vAlNzf8OgArpKRIbdtWfX7QoOjHgvAw3rt1Jk2yO4LA4ilJBwCBFBbaHUF8o1dA8DhegpXuvrv8/vr10W+4Bfv16WN3BJGXlGR3BLBLt27ejzkPhRVILCGqOneWWrYMbplTT636XFpa8Ouu2GPo9del556THn44+Hoq4yQQMcMlJSdXfToRDpCdyDO83dSp9sYR74LtbRttHNADVXFsBcCMOXOk444zRsqAb/yemjdlijHKys03G9ctKg81zWfpfJm17I4g8mrwPU5YnHciEnxcggRi10MPSXPnSpMnh1dP3bqB524Kxvjx1tQDRIrTDyCuvFKaMSO0ZTMyrI3FSo8/Lt11l9Ssmd2RxDenf/8BAEhU+fnSN9/YHQWcwuUyhpj2p2nT6MUCe8TaaQPJTACxjh5LiCs33WQc7DVsGPyynhZH/fpZGlJIcyf16mVtDEAgTh+u8YQTpP/8Rxo2LPhle/SQrrhC+vOfrY8rXC4XSaVEQOILSDw5OXZHYI3KF7xqcGYJxByOM8LjGU7/T3/iIj+A+BbrI3kgPtFjCWG76674GKopP1/au1eqXdvuSKTMTLsjQCKxYi6xWFevXmgXtFwu6dlnrY8HAAB/5s+3O4LI4KIrAFtFIIn2/PNSSYnv4fkBIJ4MGiRNn17+uEUL+2KBc5BYQkLJyrI7AiD6mKATABBPnJ6goDcqfKFnCRB70tKkLl3sjgJAsNinVlW5Ie4ZZ9gTB5yFAQuAMIVy8SOUnZynG35Fr74afD0AEC4O1K3DZ+nb4MF2RwAAAACnN/aAM9WpY3cEsafyeWePHvbEAWchsQRLzJ5t/P34Y+Ni0Jtv2hpOVEXrQCvZR//C9PTorBsAgtG+vd0RlCNxE58aN7Y7AgDB4uIjAACINl8jE6WlRT8OIBExFB7C5nJJ119v3CRp2TJ744m2UE6iGZIPsB4Xou23caO0e7fUqpXdkSDecYE6sfH/jw/8n6xFQwgAsSiRfpsSYbfmxH23E98TEC/osQRYJJgDrhYtpBkzrK2/Q4fg6oO9GjSwOwLnuftuafhwacUKuyNJXG3bSt262R1FfKlVy+4IYhMniED8YbsFYKfmze2OALAW+9XQ8dkB0UFiCbDJrbdKTz9t3L/vvvDre//98OtAYFZNtt2smbRlizV1mVF5kkanysqSFiyQBg2yOxLAPIZpABKPUy52OOV9AHCGPmfbHUFiYR8AAEiQy41A5NSuHfwynoOw0aOlPXuk8eNDW3fFXkzx1Op95Ei7IwjNH/5gTT1Nmkg1a1pTlxlJSdFbFwBYIZGGXQEAAOFLYaIHhInDTzgZ51eIBBJLQJhC6cVS8Qc9O9vcMk6al+nRR70f33WXPXFEw8MPR34dJ3Q0/nbqVPU1ekMg0Q0ebHcEAIJFK+j4VLeu3RHEty5d7I4AAAAAMI/EEoLy1Vd2RxB7GjWKznrq1YvOeqIhM9P7cevW9sQRDTfeGFz5a68Nfh2XXGz8bdOm6mtcnEMk5ObaHYF5jLcPANFh1ZDBiSo11e4IAACIP1zzAOxDZ2EEpV07uyMA4sewYb6fj+aBDwdZiITbb5c++cT/dxwAAAD2Y+gjhIvzSQCAP/RYAhAThg6NTL1z5lhXV9u25suec440f77v1xo29L8cB+6IB1lZ0ksvSSNG2B2JM9x2m90RALGlTh27I4gs9vUAAKA6HC8AiHUklhC2WNrZ2TmfzLFj9q3bCWoE8Wv0u9+ZK7dypXT11UbvCitceKH5sq1bS0lJvl+jxxKAis480/vxk0/aEwdgt9dek5Ytkxo0sDsSAHAGzgUAAECkkFgCLLJihfmyoQxJYOak4JRTgq83Ht15p7lyffsaf2+4wZr1xuOJWTzGDCS6sWPtjsB+/HYlpnPPlQYPtjsKmMV2ai3mqEIksJ0iUvhuRR6fMax02ml2RwAnIrEEWGT7drsjMJ9wCeTGG8OvIx6sXx/Z+mPlIDBW4gAAAIB/jRvbHQGcqLoGjY0aRScOOMfbb9sdAcLiwOsDXPMwJznZ7gjgRCSW4CgDB9odQXwbM0Z6+GG7o7CerxOqevWiH0e4Qjnxq1/f+jgAAAAAxL7qEksbNkQnDjjH3r12RwAAiBXkK+EoTZvaHUH8WrNG6trVnnVfcIG0bZv58mZapCxd6v+12rXNrysSQm1R07y5tHNncMv4m+cJAADYg5a1AKKlusRSnTrRiQOICyFMWQAAiYweS3CUmjXtjsBeoSZM6tSRzjhDSk21Np5g1j9+vHVzIUlSQYF1dYVr2DDfz69ZE1696enGxamsrPDqAQAAAACgsj597I4gsbRsaXcEAGAeiSU4yoQJdkcQWeee6/24SRPvx2edFXydxx8vvftu6DGZ5S9plZFh/K1ZU5o1S3rqqcjHEm2LFvl+/owzqm9FGMhPP0kHD9IrCQAAAABgvVmzpPvvtzuKxNGvX/n9iPRwdmCvLOYOAuxDYgleTjgh+GViaTiPOnWk22+3O4rqVZdM6N3bXD3t2kkLFkhvvmk8DuV/MXu2lJ8f/HLBuuIKc+XGjIlsHE6Snk4vPQAAAAC+xdK5OuJTVpZ02212R5E4aDQavBUrjPmoFyywOxIg8ZBYgpfHH7c7AkjSv/5lvuzw4aH1VIq2lBT71t2wofcBmq8TrEsuiV48gYwda3cEAAAgEjwtalu1sjcOq3DBGgAST+fOxl/2AYgVXbtK339vXBsDEF0klhDTpk+3OwL79O/v/bhHj+CWz821LhYrZGZaV1eNIH+5UlKk/fvLH/s6CK48rKBdOneWXnnF7igAAIDV9u0zbk7sbXzeeXZHAACIhkaN7I4AqIpEJ2APEkuISY0bG4mAeBjWzsPqHZmnJZAkLVsmDRoU3PKeuYtixaRJUvfu1vSKM3MwW/n/Ee2LOMF8HyoPRdiggbWxAIhtnAhVxWeSeOzs2RwtNWtKtWvbHUVkvPCC3REA8CWc+VwBRB/bLIB4QmIJMSvUHi5O2RFfeqnxt2NHafBg8xfZli2T2raVli+vWpcv0eqpk5srvf22dO210VlftDz0UPh1OLHlMgCEwyn7cpi3ZYvdESAcHMsAAAAAiYXEEmISLZWlDh2knTuljz4KbrnBg6WNG717PF1zjXeZl1+W3nxTmj9fat8+7FDjjstlzLtkhUBJO7Mqf98DXVB1aktnAEBia9zY7ggAAABiR0Sui3GtDYCFSCwBNjj5ZHPlGjb0PzRMv37m11fxgGTyZGnAAOmss6TLLjNfRzRFYziV9HQjcReO227zPyxfOAeBgRJLb7wRXF0kaQEAAAAAVnByr/KkZLsjAID4QmIJiKJPP5WeekoaOjT8upYsCb8OK40ebV1dl1wi5eRYV58/FXsthTL0Ypcuxt+HH5Zatza/XNu2wa+r4joff1x66aXQ6wAAAADgfO3a2R0BED9y69gdAYB4snbtWg0aNEiNGzeWy+XS8opzkviwevVquVyuKrcvv/wyOgFHAIklwCJmWu507CiNGRN6LxJPL6WMDOMWS+bOlR55xLr6otXT5tFHpR49pPHjvZ/v2dN8HTfeKG3aZL78H//o/TjQe73qqqrPXXutdN555tcHAABgtUsukfLyrBkWGEBk1KtndwQAgFhhdvQkmHPw4EF16tRJjwR5MfSrr75SaWlp2a1tOK3PbUZHTyCOXH+9cQLfo0fodTRoEPwyLVpIW7cGLuNySfXrhxZTtFVM5Pz+98atsocekk45JXA9oQwDMG6cVKuW+fK5ucGvAwAAINIyM6Xt26WkJLsjAeBPQYHdEQBA9Dl5yMZwXHih9PHHdkfhHP3791f//v2DXq5BgwbKicYwTVFAjyV4iZXEAPPC+JacLA0bJjVpEvyyCxZIo0ZJY8cGv+zFF5srl54efN3xIphkkJWysuxZLwAAQHVIKgGxzcnnZ7AH12oAON3+/fu1b9++stvhw4ctrb+wsFB5eXnq06ePVq1aZWnd0UZiCZKkFSuMuX+cMAYzLRPKDR5stFLr3l0aPlyaN09KTY3c+nr3jlzddtq/X9q9O7hlrDrgvvlma+oBEsldd9kdAQAAQOx69lmppMTuKAAAiD0FBQXKzs4uu02bNs2SevPy8jRnzhwtWbJES5cuVX5+vvr06aO1a9daUr8dGAoPkqRBg+yOwFmqG0LNF89wc8kWbpVLlxqJthphppDNJkmCabXao4f09tvSyJHmyt96q/TAA9KkSebK5+X5f61xY3N1eGRmlt9ftMj6uQQCfb4V1w3AnGC3ccQeWsMCABA5V1xhdwQAqsPxMKzGd8qcDRs2qEmFoaLS0tIsqTc/P1/5+fllj7t166bt27drxowZOuOMMyxZR7TRYwkxKRF/7FauNIac+/BD6+p0ucJPKkXKSy9Jf/+79OCD5srfd5+0YYM0dWrgctu3S99+K9WuXfW1VaukPn2kJUuCj9dj2LDQl5WkTp3CWx6A8yTiPg8AAABwqpoZdkcAIFS1a9dWVlZW2c2qxJIvXbt21ddffx2x+iONHksIGxfErJGfbyRaEkV2duC5m447Tvroo/LHNWpIxx9ffb1Nm/p/rVcv42aH4mLprbekK6+s+lrdutGPB0DsYD8KAAAAOEf7fKn4qN1RAIh1xcXFygs05FKMI7GEmBTORbZYmGMpFmKIdy+8YCSX7GL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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x600 with 2 Axes>",
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function flush_figures at 0x7f84c6d8b310> (for post_execute):\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:126\u001B[0m, in \u001B[0;36mflush_figures\u001B[0;34m()\u001B[0m\n\u001B[1;32m    123\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m InlineBackend\u001B[38;5;241m.\u001B[39minstance()\u001B[38;5;241m.\u001B[39mclose_figures:\n\u001B[1;32m    124\u001B[0m     \u001B[38;5;66;03m# ignore the tracking, just draw and close all figures\u001B[39;00m\n\u001B[1;32m    125\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 126\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mshow\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m    127\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m    128\u001B[0m         \u001B[38;5;66;03m# safely show traceback if in IPython, else raise\u001B[39;00m\n\u001B[1;32m    129\u001B[0m         ip \u001B[38;5;241m=\u001B[39m get_ipython()\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib_inline/backend_inline.py:90\u001B[0m, in \u001B[0;36mshow\u001B[0;34m(close, block)\u001B[0m\n\u001B[1;32m     88\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m     89\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m figure_manager \u001B[38;5;129;01min\u001B[39;00m Gcf\u001B[38;5;241m.\u001B[39mget_all_fig_managers():\n\u001B[0;32m---> 90\u001B[0m         \u001B[43mdisplay\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m     91\u001B[0m \u001B[43m            \u001B[49m\u001B[43mfigure_manager\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcanvas\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfigure\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m     92\u001B[0m \u001B[43m            \u001B[49m\u001B[43mmetadata\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m_fetch_figure_metadata\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfigure_manager\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcanvas\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfigure\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     93\u001B[0m \u001B[43m        \u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     94\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m     95\u001B[0m     show\u001B[38;5;241m.\u001B[39m_to_draw \u001B[38;5;241m=\u001B[39m []\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/IPython/core/display_functions.py:298\u001B[0m, in \u001B[0;36mdisplay\u001B[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001B[0m\n\u001B[1;32m    296\u001B[0m     publish_display_data(data\u001B[38;5;241m=\u001B[39mobj, metadata\u001B[38;5;241m=\u001B[39mmetadata, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m    297\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m--> 298\u001B[0m     format_dict, md_dict \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mformat\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43minclude\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minclude\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mexclude\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mexclude\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    299\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m format_dict:\n\u001B[1;32m    300\u001B[0m         \u001B[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001B[39;00m\n\u001B[1;32m    301\u001B[0m         \u001B[38;5;28;01mcontinue\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/IPython/core/formatters.py:179\u001B[0m, in \u001B[0;36mDisplayFormatter.format\u001B[0;34m(self, obj, include, exclude)\u001B[0m\n\u001B[1;32m    177\u001B[0m md \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m    178\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 179\u001B[0m     data \u001B[38;5;241m=\u001B[39m \u001B[43mformatter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    180\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m:\n\u001B[1;32m    181\u001B[0m     \u001B[38;5;66;03m# FIXME: log the exception\u001B[39;00m\n\u001B[1;32m    182\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/decorator.py:232\u001B[0m, in \u001B[0;36mdecorate.<locals>.fun\u001B[0;34m(*args, **kw)\u001B[0m\n\u001B[1;32m    230\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m kwsyntax:\n\u001B[1;32m    231\u001B[0m     args, kw \u001B[38;5;241m=\u001B[39m fix(args, kw, sig)\n\u001B[0;32m--> 232\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mcaller\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mextras\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m+\u001B[39;49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/IPython/core/formatters.py:223\u001B[0m, in \u001B[0;36mcatch_format_error\u001B[0;34m(method, self, *args, **kwargs)\u001B[0m\n\u001B[1;32m    221\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001B[39;00m\n\u001B[1;32m    222\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m--> 223\u001B[0m     r \u001B[38;5;241m=\u001B[39m \u001B[43mmethod\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    224\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m:\n\u001B[1;32m    225\u001B[0m     \u001B[38;5;66;03m# don't warn on NotImplementedErrors\u001B[39;00m\n\u001B[1;32m    226\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_check_return(\u001B[38;5;28;01mNone\u001B[39;00m, args[\u001B[38;5;241m0\u001B[39m])\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/IPython/core/formatters.py:340\u001B[0m, in \u001B[0;36mBaseFormatter.__call__\u001B[0;34m(self, obj)\u001B[0m\n\u001B[1;32m    338\u001B[0m     \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[1;32m    339\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m--> 340\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mprinter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mobj\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    341\u001B[0m \u001B[38;5;66;03m# Finally look for special method names\u001B[39;00m\n\u001B[1;32m    342\u001B[0m method \u001B[38;5;241m=\u001B[39m get_real_method(obj, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprint_method)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/IPython/core/pylabtools.py:152\u001B[0m, in \u001B[0;36mprint_figure\u001B[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001B[0m\n\u001B[1;32m    149\u001B[0m     \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mbackend_bases\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m FigureCanvasBase\n\u001B[1;32m    150\u001B[0m     FigureCanvasBase(fig)\n\u001B[0;32m--> 152\u001B[0m \u001B[43mfig\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcanvas\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprint_figure\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbytes_io\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkw\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    153\u001B[0m data \u001B[38;5;241m=\u001B[39m bytes_io\u001B[38;5;241m.\u001B[39mgetvalue()\n\u001B[1;32m    154\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m fmt \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124msvg\u001B[39m\u001B[38;5;124m'\u001B[39m:\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/backend_bases.py:2342\u001B[0m, in \u001B[0;36mFigureCanvasBase.print_figure\u001B[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001B[0m\n\u001B[1;32m   2336\u001B[0m     renderer \u001B[38;5;241m=\u001B[39m _get_renderer(\n\u001B[1;32m   2337\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfigure,\n\u001B[1;32m   2338\u001B[0m         functools\u001B[38;5;241m.\u001B[39mpartial(\n\u001B[1;32m   2339\u001B[0m             print_method, orientation\u001B[38;5;241m=\u001B[39morientation)\n\u001B[1;32m   2340\u001B[0m     )\n\u001B[1;32m   2341\u001B[0m     \u001B[38;5;28;01mwith\u001B[39;00m \u001B[38;5;28mgetattr\u001B[39m(renderer, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m_draw_disabled\u001B[39m\u001B[38;5;124m\"\u001B[39m, nullcontext)():\n\u001B[0;32m-> 2342\u001B[0m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfigure\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   2344\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m bbox_inches:\n\u001B[1;32m   2345\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m bbox_inches \u001B[38;5;241m==\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtight\u001B[39m\u001B[38;5;124m\"\u001B[39m:\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/artist.py:95\u001B[0m, in \u001B[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001B[0;34m(artist, renderer, *args, **kwargs)\u001B[0m\n\u001B[1;32m     93\u001B[0m \u001B[38;5;129m@wraps\u001B[39m(draw)\n\u001B[1;32m     94\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mdraw_wrapper\u001B[39m(artist, renderer, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m---> 95\u001B[0m     result \u001B[38;5;241m=\u001B[39m \u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43martist\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     96\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m renderer\u001B[38;5;241m.\u001B[39m_rasterizing:\n\u001B[1;32m     97\u001B[0m         renderer\u001B[38;5;241m.\u001B[39mstop_rasterizing()\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/artist.py:72\u001B[0m, in \u001B[0;36mallow_rasterization.<locals>.draw_wrapper\u001B[0;34m(artist, renderer)\u001B[0m\n\u001B[1;32m     69\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m artist\u001B[38;5;241m.\u001B[39mget_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m     70\u001B[0m         renderer\u001B[38;5;241m.\u001B[39mstart_filter()\n\u001B[0;32m---> 72\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43martist\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     73\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m     74\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m artist\u001B[38;5;241m.\u001B[39mget_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/figure.py:3140\u001B[0m, in \u001B[0;36mFigure.draw\u001B[0;34m(self, renderer)\u001B[0m\n\u001B[1;32m   3137\u001B[0m         \u001B[38;5;66;03m# ValueError can occur when resizing a window.\u001B[39;00m\n\u001B[1;32m   3139\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpatch\u001B[38;5;241m.\u001B[39mdraw(renderer)\n\u001B[0;32m-> 3140\u001B[0m \u001B[43mmimage\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_draw_list_compositing_images\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   3141\u001B[0m \u001B[43m    \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43martists\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msuppressComposite\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3143\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m sfig \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msubfigs:\n\u001B[1;32m   3144\u001B[0m     sfig\u001B[38;5;241m.\u001B[39mdraw(renderer)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/image.py:131\u001B[0m, in \u001B[0;36m_draw_list_compositing_images\u001B[0;34m(renderer, parent, artists, suppress_composite)\u001B[0m\n\u001B[1;32m    129\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m not_composite \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m has_images:\n\u001B[1;32m    130\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m artists:\n\u001B[0;32m--> 131\u001B[0m         \u001B[43ma\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    132\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    133\u001B[0m     \u001B[38;5;66;03m# Composite any adjacent images together\u001B[39;00m\n\u001B[1;32m    134\u001B[0m     image_group \u001B[38;5;241m=\u001B[39m []\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/artist.py:72\u001B[0m, in \u001B[0;36mallow_rasterization.<locals>.draw_wrapper\u001B[0;34m(artist, renderer)\u001B[0m\n\u001B[1;32m     69\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m artist\u001B[38;5;241m.\u001B[39mget_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m     70\u001B[0m         renderer\u001B[38;5;241m.\u001B[39mstart_filter()\n\u001B[0;32m---> 72\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43martist\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     73\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m     74\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m artist\u001B[38;5;241m.\u001B[39mget_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/axes/_base.py:3064\u001B[0m, in \u001B[0;36m_AxesBase.draw\u001B[0;34m(self, renderer)\u001B[0m\n\u001B[1;32m   3061\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m artists_rasterized:\n\u001B[1;32m   3062\u001B[0m     _draw_rasterized(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfigure, artists_rasterized, renderer)\n\u001B[0;32m-> 3064\u001B[0m \u001B[43mmimage\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_draw_list_compositing_images\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m   3065\u001B[0m \u001B[43m    \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43martists\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfigure\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msuppressComposite\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m   3067\u001B[0m renderer\u001B[38;5;241m.\u001B[39mclose_group(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124maxes\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m   3068\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstale \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/image.py:131\u001B[0m, in \u001B[0;36m_draw_list_compositing_images\u001B[0;34m(renderer, parent, artists, suppress_composite)\u001B[0m\n\u001B[1;32m    129\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m not_composite \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m has_images:\n\u001B[1;32m    130\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m a \u001B[38;5;129;01min\u001B[39;00m artists:\n\u001B[0;32m--> 131\u001B[0m         \u001B[43ma\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    132\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m    133\u001B[0m     \u001B[38;5;66;03m# Composite any adjacent images together\u001B[39;00m\n\u001B[1;32m    134\u001B[0m     image_group \u001B[38;5;241m=\u001B[39m []\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/artist.py:72\u001B[0m, in \u001B[0;36mallow_rasterization.<locals>.draw_wrapper\u001B[0;34m(artist, renderer)\u001B[0m\n\u001B[1;32m     69\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m artist\u001B[38;5;241m.\u001B[39mget_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m     70\u001B[0m         renderer\u001B[38;5;241m.\u001B[39mstart_filter()\n\u001B[0;32m---> 72\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mdraw\u001B[49m\u001B[43m(\u001B[49m\u001B[43martist\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m     73\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m     74\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m artist\u001B[38;5;241m.\u001B[39mget_agg_filter() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/legend.py:726\u001B[0m, in \u001B[0;36mLegend.draw\u001B[0;34m(self, renderer)\u001B[0m\n\u001B[1;32m    722\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_legend_box\u001B[38;5;241m.\u001B[39mset_width(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_bbox_to_anchor()\u001B[38;5;241m.\u001B[39mwidth \u001B[38;5;241m-\u001B[39m pad)\n\u001B[1;32m    724\u001B[0m \u001B[38;5;66;03m# update the location and size of the legend. This needs to\u001B[39;00m\n\u001B[1;32m    725\u001B[0m \u001B[38;5;66;03m# be done in any case to clip the figure right.\u001B[39;00m\n\u001B[0;32m--> 726\u001B[0m bbox \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_legend_box\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_window_extent\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    727\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlegendPatch\u001B[38;5;241m.\u001B[39mset_bounds(bbox\u001B[38;5;241m.\u001B[39mbounds)\n\u001B[1;32m    728\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mlegendPatch\u001B[38;5;241m.\u001B[39mset_mutation_scale(fontsize)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/offsetbox.py:402\u001B[0m, in \u001B[0;36mOffsetBox.get_window_extent\u001B[0;34m(self, renderer)\u001B[0m\n\u001B[1;32m    400\u001B[0m bbox \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_bbox(renderer)\n\u001B[1;32m    401\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:  \u001B[38;5;66;03m# Some subclasses redefine get_offset to take no args.\u001B[39;00m\n\u001B[0;32m--> 402\u001B[0m     px, py \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mget_offset\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbbox\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    403\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m:\n\u001B[1;32m    404\u001B[0m     px, py \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mget_offset()\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/offsetbox.py:60\u001B[0m, in \u001B[0;36m_compat_get_offset.<locals>.get_offset\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m     56\u001B[0m params \u001B[38;5;241m=\u001B[39m _api\u001B[38;5;241m.\u001B[39mselect_matching_signature(sigs, \u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m     57\u001B[0m bbox \u001B[38;5;241m=\u001B[39m (params[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbbox\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mbbox\u001B[39m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;129;01min\u001B[39;00m params \u001B[38;5;28;01melse\u001B[39;00m\n\u001B[1;32m     58\u001B[0m         Bbox\u001B[38;5;241m.\u001B[39mfrom_bounds(\u001B[38;5;241m-\u001B[39mparams[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mxdescent\u001B[39m\u001B[38;5;124m\"\u001B[39m], \u001B[38;5;241m-\u001B[39mparams[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mydescent\u001B[39m\u001B[38;5;124m\"\u001B[39m],\n\u001B[1;32m     59\u001B[0m                          params[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mwidth\u001B[39m\u001B[38;5;124m\"\u001B[39m], params[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mheight\u001B[39m\u001B[38;5;124m\"\u001B[39m]))\n\u001B[0;32m---> 60\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mmeth\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparams\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mself\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbbox\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mparams\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mrenderer\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/offsetbox.py:313\u001B[0m, in \u001B[0;36mOffsetBox.get_offset\u001B[0;34m(self, bbox, renderer)\u001B[0m\n\u001B[1;32m    298\u001B[0m \u001B[38;5;129m@_compat_get_offset\u001B[39m\n\u001B[1;32m    299\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mget_offset\u001B[39m(\u001B[38;5;28mself\u001B[39m, bbox, renderer):\n\u001B[1;32m    300\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    301\u001B[0m \u001B[38;5;124;03m    Return the offset as a tuple (x, y).\u001B[39;00m\n\u001B[1;32m    302\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    310\u001B[0m \u001B[38;5;124;03m    renderer : `.RendererBase` subclass\u001B[39;00m\n\u001B[1;32m    311\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m    312\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m (\n\u001B[0;32m--> 313\u001B[0m         \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_offset\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mwidth\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mheight\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mx0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43my0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    314\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mcallable\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_offset)\n\u001B[1;32m    315\u001B[0m         \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_offset)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/legend.py:695\u001B[0m, in \u001B[0;36mLegend._findoffset\u001B[0;34m(self, width, height, xdescent, ydescent, renderer)\u001B[0m\n\u001B[1;32m    692\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Helper function to locate the legend.\"\"\"\u001B[39;00m\n\u001B[1;32m    694\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_loc \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:  \u001B[38;5;66;03m# \"best\".\u001B[39;00m\n\u001B[0;32m--> 695\u001B[0m     x, y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_find_best_position\u001B[49m\u001B[43m(\u001B[49m\u001B[43mwidth\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mheight\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mrenderer\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m    696\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_loc \u001B[38;5;129;01min\u001B[39;00m Legend\u001B[38;5;241m.\u001B[39mcodes\u001B[38;5;241m.\u001B[39mvalues():  \u001B[38;5;66;03m# Fixed location.\u001B[39;00m\n\u001B[1;32m    697\u001B[0m     bbox \u001B[38;5;241m=\u001B[39m Bbox\u001B[38;5;241m.\u001B[39mfrom_bounds(\u001B[38;5;241m0\u001B[39m, \u001B[38;5;241m0\u001B[39m, width, height)\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/legend.py:1144\u001B[0m, in \u001B[0;36mLegend._find_best_position\u001B[0;34m(self, width, height, renderer, consider)\u001B[0m\n\u001B[1;32m   1137\u001B[0m badness \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[1;32m   1138\u001B[0m \u001B[38;5;66;03m# XXX TODO: If markers are present, it would be good to take them\u001B[39;00m\n\u001B[1;32m   1139\u001B[0m \u001B[38;5;66;03m# into account when checking vertex overlaps in the next line.\u001B[39;00m\n\u001B[1;32m   1140\u001B[0m badness \u001B[38;5;241m=\u001B[39m (\u001B[38;5;28msum\u001B[39m(legendBox\u001B[38;5;241m.\u001B[39mcount_contains(line\u001B[38;5;241m.\u001B[39mvertices)\n\u001B[1;32m   1141\u001B[0m                \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines)\n\u001B[1;32m   1142\u001B[0m            \u001B[38;5;241m+\u001B[39m legendBox\u001B[38;5;241m.\u001B[39mcount_contains(offsets)\n\u001B[1;32m   1143\u001B[0m            \u001B[38;5;241m+\u001B[39m legendBox\u001B[38;5;241m.\u001B[39mcount_overlaps(bboxes)\n\u001B[0;32m-> 1144\u001B[0m            \u001B[38;5;241m+\u001B[39m \u001B[38;5;28;43msum\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mintersects_bbox\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlegendBox\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilled\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m   1145\u001B[0m \u001B[43m                 \u001B[49m\u001B[38;5;28;43;01mfor\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mline\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01min\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mlines\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[1;32m   1146\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m badness \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m   1147\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m l, b\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/legend.py:1144\u001B[0m, in \u001B[0;36m<genexpr>\u001B[0;34m(.0)\u001B[0m\n\u001B[1;32m   1137\u001B[0m badness \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[1;32m   1138\u001B[0m \u001B[38;5;66;03m# XXX TODO: If markers are present, it would be good to take them\u001B[39;00m\n\u001B[1;32m   1139\u001B[0m \u001B[38;5;66;03m# into account when checking vertex overlaps in the next line.\u001B[39;00m\n\u001B[1;32m   1140\u001B[0m badness \u001B[38;5;241m=\u001B[39m (\u001B[38;5;28msum\u001B[39m(legendBox\u001B[38;5;241m.\u001B[39mcount_contains(line\u001B[38;5;241m.\u001B[39mvertices)\n\u001B[1;32m   1141\u001B[0m                \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines)\n\u001B[1;32m   1142\u001B[0m            \u001B[38;5;241m+\u001B[39m legendBox\u001B[38;5;241m.\u001B[39mcount_contains(offsets)\n\u001B[1;32m   1143\u001B[0m            \u001B[38;5;241m+\u001B[39m legendBox\u001B[38;5;241m.\u001B[39mcount_overlaps(bboxes)\n\u001B[0;32m-> 1144\u001B[0m            \u001B[38;5;241m+\u001B[39m \u001B[38;5;28msum\u001B[39m(\u001B[43mline\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mintersects_bbox\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlegendBox\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilled\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43;01mFalse\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m   1145\u001B[0m                  \u001B[38;5;28;01mfor\u001B[39;00m line \u001B[38;5;129;01min\u001B[39;00m lines))\n\u001B[1;32m   1146\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m badness \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m   1147\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m l, b\n",
      "File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.9/site-packages/matplotlib/path.py:662\u001B[0m, in \u001B[0;36mPath.intersects_bbox\u001B[0;34m(self, bbox, filled)\u001B[0m\n\u001B[1;32m    653\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mintersects_bbox\u001B[39m(\u001B[38;5;28mself\u001B[39m, bbox, filled\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m):\n\u001B[1;32m    654\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[1;32m    655\u001B[0m \u001B[38;5;124;03m    Return whether this path intersects a given `~.transforms.Bbox`.\u001B[39;00m\n\u001B[1;32m    656\u001B[0m \n\u001B[0;32m   (...)\u001B[0m\n\u001B[1;32m    660\u001B[0m \u001B[38;5;124;03m    The bounding box is always considered filled.\u001B[39;00m\n\u001B[1;32m    661\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[0;32m--> 662\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43m_path\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpath_intersects_rectangle\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m    663\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mx0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43my0\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mx1\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbbox\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43my1\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilled\u001B[49m\u001B[43m)\u001B[49m\n",
      "\u001B[0;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "for i, name in enumerate(edfs):\n",
    "    fig, ax = plt.subplots(figsize=(20, 6))\n",
    "    y = data[name].flatten()\n",
    "    y_stages = labels[name]\n",
    "    y_stages = m.three_stages_transform(y_stages)\n",
    "    y_stages = np.repeat(y_stages, 10000)\n",
    "    draw_hypnogram(y, y_stages, ax, name)\n",
    "    plt.savefig(f'images/{name}_{len(set(y_stages))}_stages.png')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:47:04.419165871Z",
     "start_time": "2023-08-13T19:30:09.400072527Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "def where_only_one_spike(data, max= 0.0033):\n",
    "    hits = []\n",
    "    for i in range(1, len(data) - 1):\n",
    "        prev = abs(data[i - 1])\n",
    "        cur = abs(data[i])\n",
    "        nextt = abs(data[i + 1])\n",
    "        if cur == max and prev < max and nextt < max:\n",
    "            hits.append(i)\n",
    "\n",
    "    return hits\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:47:04.628803101Z",
     "start_time": "2023-08-13T19:47:04.494351187Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "5466"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "possible_interpolation = where_only_one_spike(all_data.flatten())\n",
    "len(possible_interpolation)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:48:30.900050938Z",
     "start_time": "2023-08-13T19:47:04.494695266Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "How does the signal look around the point thatis in the max?"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f84c0ed5910>]"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_data.flatten()[13098015:13099915])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:48:31.182452542Z",
     "start_time": "2023-08-13T19:48:30.881392030Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "start = 13099215\n",
    "stop = 13099415"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:48:31.185156945Z",
     "start_time": "2023-08-13T19:48:31.182817641Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f84c0e36370>]"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(start, stop),all_data.flatten()[start:stop] )"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:48:31.522461525Z",
     "start_time": "2023-08-13T19:48:31.187291700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "-0.0033"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.flatten()[13099315]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-13T19:48:31.785388584Z",
     "start_time": "2023-08-13T19:48:31.522986884Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "There are multiple moments in the where thre are big spikes"
   ],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
